Update README.md

README.md

@@ -3,7 +3,7 @@ datasets:
 - monology/pile-uncopyrighted
 language:
 - en
-library_name: CALM
+library_name: transformers
 license: mit
 metrics:
 - BrierLM
@@ -12,7 +12,6 @@ tags:
 - language modeling
 pipeline_tag: text-generation
 ---
-
 # Continuous Autoregressive Language Models
 
 [![Paper](https://img.shields.io/badge/Paper_📃-green)](https://arxiv.org/abs/2510.27688)
@@ -20,25 +19,26 @@ pipeline_tag: text-generation
 [![HuggingFace](https://img.shields.io/badge/HuggingFace_🤗-orange)](https://huggingface.co/collections/cccczshao/calm)
 [![Blog](https://img.shields.io/badge/Blog_✍️-yellowgreen)](https://shaochenze.github.io/blog/2025/CALM/)
 
+
 ## Model Description
 
 Modern Large Language Models (LLMs) are constrained by a fundamental bottleneck: they generate text one token at a time. **CALM (Continuous Autoregressive Language Models)** confronts this challenge by introducing a paradigm shift in language modeling. Instead of predicting one discrete token at a time, CALM learns to predict a single continuous vector that represents an entire chunk of K tokens.
 
 This is achieved through a two-stage process:
 
-1.  **A high-fidelity autoencoder** learns to compress K tokens into a single vector and reconstruct them with near-perfect accuracy.
-2.  **A continuous-domain language model** then performs autoregressive prediction in this vector space.
+1. **A high-fidelity autoencoder** learns to compress K tokens into a single vector and reconstruct them with near-perfect accuracy.
+2. **A continuous-domain language model** then performs autoregressive prediction in this vector space.
 
 ### Key Features
 
-*   🚀 **Ultra-Efficient by Design:** Dramatically improves training and inference efficiency by reducing the number of autoregressive steps by a factor of K.
-*   💡 **A New Scaling Axis:** Introduces a new scaling dimension for LLMs—semantic bandwidth (K). Instead of just scaling parameters and data, you can now scale the amount of information processed in a single step.
-*   🛠️ **A Comprehensive Likelihood-Free Toolkit:** Operating in a continuous domain requires new tools. This repository provides the full suite of algorithms that make CALM possible:
+* 🚀 **Ultra-Efficient by Design:** Dramatically improves training and inference efficiency by reducing the number of autoregressive steps by a factor of K.
+* 💡 **A New Scaling Axis:** Introduces a new scaling dimension for LLMs—semantic bandwidth (K). Instead of just scaling parameters and data, you can now scale the amount of information processed in a single step.
+* 🛠️ **A Comprehensive Likelihood-Free Toolkit:** Operating in a continuous domain requires new tools. This repository provides the full suite of algorithms that make CALM possible:
   
-  *   **A Robust Autoencoder** to learn high-fidelity continuous representations of token chunks.
-  *   **Energy-Based Training**, a principled and likelihood-free method for generative modeling.
-  *   **BrierLM**, a new metric for calibrated, likelihood-free evaluation of language models.
-  *   **Temperature Sampling** for controlled, high-quality text generation using only a black-box sampler.
+  * **A Robust Autoencoder** to learn high-fidelity continuous representations of token chunks.
+  * **Energy-Based Training**, a principled and likelihood-free method for generative modeling.
+  * **BrierLM**, a new metric for calibrated, likelihood-free evaluation of language models.
+  * **Temperature Sampling** for controlled, high-quality text generation using only a black-box sampler.
 
 ## How to use